Students demonstrate how analytics underlie strong dataviz
Jul 01, 2025
In today's post, I'm delighted to feature work by several students of Ray Vella's data visualization class at NYU. They have been asked to improve the following Economist chart entitled "The Rich Get Richer".
In my guest lecture to the class, I emphasized the importance of upfront analytics when constructing data visualizations.
One of the key messages is pay attention to definitions. How does the Economist define "rich" and "poor"? (it's not what you think). Instead of using percentiles (e.g. top 1% of the income distribution), they define "rich" as people living in the richest region by average GDP, and "poor" as people living in the poorest region by average GDP. Thus, the "gap" between the rich and the poor is measured by the difference in GDP between the average persons in those two regions.
I don't like this metric at all but we'll just have to accept that that's the data available for the class assignment.
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Shulin Huang's work is notable in how she clarifies the underlying algebra.
The middle section classifies the countries into two groups, those with widening vs narrowing gaps. The side panels show the two components of the gap change. The gap change is the sum of the change in the richest region and the change in the poorest region.
If we take the U.S. as an example, the gap increased by 1976 units. This is because the richest region gained 1777 while the poor region lost 199. Germany has a very different experience: the richest region regressed by 2215 while the poorest region improved by 424, leading to the gap narrowing by 2638.
Note how important it is to keep the order of the countries fixed across all three panels. I'm not sure how she decided the order of these countries, which is a small oversight in an otherwise excellent effort.
Shulin's text is very thoughtful throughout. The chart title clearly states "rich regions" rather than "the rich". Take a look at the bottom of the side panels. The label "national AVG" shows that the zero level is the national average. Then, the label "regions pulled further ahead" perfectly captures the positive direction.
Compared to the original, this chart is much more easily understood. The secret is the clarity of thought, the deep understanding of the nature of the data.
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Michael Unger focuses his work on elucidating the indexing strategy employed by the Economist. In the original, each value of regional average GDP is indexed to the national average of the relevant year. A number like 150 means the region has an average GDP for the given year that is 50% higher than the national average. It's tough to explain how such indices work.
Michael's revision goes back to the raw data. He presents them in two panels. On the left, the absolute change over time in the average GDPs are presented for each of the richest/poorest region while on the right, the relative change is shown.
(Some of the country labels are incorrect. I'll replace with a corrected version when I receive one.)
Presenting both sides is not redundant. In France, for example, the richest region improved by 17K while the poorest region went up by not quite 6K. But 6K on a much lower base represents a much higher proportional jump as the right side shows.
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Related to Michael's work, but even simpler, is Debbie Hsieh's effort.
Debbie reduces the entire exercise to one message - the relative change over time in average GDP between the richest and poorest region in each country. In this simplest presentation, if both columns point up, then both the richest and the poorest region increased their average GDP; if both point down, then both regions suffered GDP drops.
If the GDP increased in the richest region while it decreased in the poorest region, then the gap widened by the most. This is represented by the blue column pointing up and the red column pointing down.
In some countries (e.g. Sweden), the poorest region (orange) got worse while the richest region (blue) improved slightly. In Italy and Spain, both the best and worst regions gained in average GDPs although the richest region attained a greater relative gain.
While Debbie's chart is simpler, it hides something that Michael's work shows more clearly. If both the richest and poorest regions increased GDP by the same percentage amount, the average person in the richest region actually experienced a higher absolute increase because the base of the percentage is higher.
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The numbers across these charts aren't necessarily well aligned. That's actually one of the challenges of this dataset. There are many ways to process the data, and small differences in how each student handles the data lead to differences in the derived values, resulting in differences in the visual effects.